Conceptualization of an ICU Infrastructure for Simulation Based Education in Medical Engineering & eHealth.

The use of simulation-based training is gaining importance in medical as well as engineering related education. The complex environment of an intensive care unit is characterized by a high need of interaction between clinical as well as technical components and views. These diverse interactions and the connected requirements are the focus for the presented simulation infrastructure, enabling research, education and training. The presented concept of a modular and flexible intensive care environment provides a high degree of interoperability and flexibility for individual research questions and full support of connectivity for typical clinical workflows. The presented simulation and testing bed will allow both, education for engineering and medical students using patient simulation and simultaneous data transfer as well as research on medical workflows, infrastructural demands and connectivity conformance questions.

Evaluating wearable multimodal sensor insoles for motion-pattern measurements in stroke rehabilitation - A pilot study.

The majority of stroke patients experience deficits in motoric functions, especially in gait and mobility. They need rehabilitation to regain walking independence, which is a major goal of rehabilitation after stroke. To document and assess the rehabilitation progress, instrumented motion analysis and clinical assessments are commonly used. In a clinical pilot study the applicability of an instrumented insole system in stroke rehabilitation is evaluated. Motion parameter of 35 stroke patients were gathered with the system while completing 90 s level walking and Timed Up & Go test at the beginning and end of four weeks inpatient rehabilitation. For level walking the motion parameter were gathered with the clinical reference system simultaneously. The mean stride time for level walking decreased from 1.20 s to 1.16 s (clinical system), or from 1.19 s to 1.12 s (insole system), respectively. Focusing on individual comparison of each patient's progress, 9 gait parameters are extracted for level walking, 6 sub-phases of Timed Up & Go test are detected and analyzed, as well as progress of Center of Pressure in the sub-phases is examined. Although the overall data show wide distribution, the system proofed to be applicable in clinical stroke rehabilitation routine. As the system is location-independent, and has the advantage of assessing additional parameters of the Timed Up & Go test, it is additionally suitable for integration in a tele-or home rehabilitation system.